Software metadata

BioHackOutcomes

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name	BioHackOutcomes
description	BioHackathon project to define and follow up BioHackathon projects.
url	https://github.com/zbmed-semtec/BioHackOutcomes
author	Visit Person Object familyName Lazarov givenName Georgi Visit Person Object familyName Castro givenName Leyla Jael
license	Visit CreativeWork Object name APACHE LICENSE, VERSION 2.0 url https://www.apache.org/licenses/LICENSE-2.0
citation	Georgi L, Castro LJ. BioHackathon Outcomes GitHub Medatada. GitHub 2020. https://github.com/zbmed-semtec/BioHackOutcomes Castro LJ, Martin C, Lazarov G, Cernoskova D, Takatsuki T, Harrow J, and Rebholz-Schuhmann D. (2021, August 10). Measuring outcomes and impact from the BioHackathon Europe. https://doi.org/10.37044/osf.io/3dxhg
codeRepository	https://github.com/zbmed-semtec/BioHackOutcomes
programmingLanguage	Python
keywords	BioHackathon Europe 2020 GitHub Metrics

Metadata model for machine-actionable Software Management Plans

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name	Metadata model for machine-actionable Software Management Plans
description	This project corresponds to an extension of the Research Data Alliance (RDA) machine-actionable Data Management Plan (maDMP) application profile and its corresponding DMP Common Standard ontology (DCSO) in order to cover the case of ELIXIR Software Management Plans (SMPs). Similar to DMPs, SMPs help formalize a set of structures and goals that ensure the software is accessible and reusable in the short, medium and long term. Although targeting the life sciences community, most of the elements of the ELIXIR SMPs are domain agnostic and could be used by other communities as well. DMPs and SMPs can be presented as text-based documents, sometimes guided by a set of questions corresponding to key points related to the lifecycle of either data or software. The RDA DMP Common Standards working group defined a maDMP to overcome limitations of text-based documents. We propose a similar path for the ELIXIR SMPs so they turn into machine-actionable SMPs (maSMPs).
url	https://github.com/zbmed-semtec/maSMPs
author	Visit Person Object familyName Giraldo givenName Olga Visit Person Object familyName Geist givenName Lukas Visit Person Object familyName Quiñones givenName Nelson Visit Person Object familyName Solanki givenName Dhwani Visit Person Object familyName Rebholz-Schuhmann givenName Dietrich Visit Person Object familyName Castro givenName Leyla Jael
license	Visit CreativeWork Object name Creative Commons Attribution 4.0 International alternateName CC BY 4.0 url https://creativecommons.org/licenses/by/4.0/
citation	Giraldo O, Geist L, Quiñones N, Solanki D, Rebholz-Schuhmann D, Castro LJ. machine-actionable Software Management Plan Ontology (maSMP Ontology). Zenodo; 2023. doi:10.5281/zenodo.8089518
codeRepository	https://github.com/zbmed-semtec/maSMPs
programmingLanguage	JavaScript Python
keywords	Software Management Plan Machine-actionable SMP

Mowl-graph2doc2vec

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name	Mowl-graph2doc2vec
description	A repository to explore the use of ontology-based graphs (generated with MOWL) as background knowledge for scientific articles similarity.
url	https://github.com/zbmed-semtec/mowl-graph2doc2vec
author	Visit Person Object familyName Quiñones givenName Nelson Visit Person Object familyName Alayed givenName Rahaf Visit Person Object familyName Aljohani givenName Layan Visit Person Object familyName Alqhtani givenName Asmaa Visit Person Object familyName Gomez Castillo givenName Maria G. Visit Person Object familyName Castro givenName Leyla Jael
license	Visit CreativeWork Object name GPLv3 url https://www.gnu.org/licenses/gpl-3.0.en.html
codeRepository	https://github.com/zbmed-semtec/mowl-graph2doc2vec
programmingLanguage	JavaScript Python
keywords	BioHackathon MENA 2023 Graph embeddings Word embeddings

Protein-function-embeddings-thesis

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name	Protein-function-embeddings-thesis
description	This thesis explores how information for protein functions can be exploited through embeddings so that the produced information can be used to improve protein function annotations. The underlying hypothesis here is that any pair of proteins with high sequence similarity will also share a similar biological function which would be reflected by the corresponding protein embeddings. The comparion and evaluation of this is done using two text-driven embedding approaches: Word2doc2Vec and Hybrid-Word2doc2Vec.
url	https://github.com/zbmed-semtec/protein-function-embeddings-thesis
author	Visit Person Object familyName Ravinder givenName Rohitha Visit Person Object familyName Castro givenName Leyla Jael Visit Person Object familyName Rebholz-Schuhmann givenName Dietrich
license	Visit CreativeWork Object name GPLv3 url https://www.gnu.org/licenses/gpl-3.0.en.html
citation	Ravinder R, Castro LJ, and Rebholz-Schuhmann D. (2023). Protein Function Embeddings: First Beta Release of Datasets (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7793384 Ravinder R, Castro LJ, and Rebholz-Schuhmann D. (2023). Protein Function Embeddings: First Beta Release (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.7781870
codeRepository	https://github.com/zbmed-semtec/protein-function-embeddings-thesis
programmingLanguage	Python
keywords	Protein Function Word2doc2Vec Hybrid-Word2doc2Vec

Topic-categorization-system

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name	Topic-categorization-system
description	Topic categorization on scientific papers to improve information retrieval in the biomedical domain.
url	https://github.com/zbmed-semtec/topic-categorization-system
author	Visit Person Object familyName Quiñones givenName Nelson Visit Person Object familyName Canales givenName Cesar Visit Person Object familyName Torres givenName Javier Visit Person Object familyName Rebholz-Schuhmann givenName Dietrich Visit Person Object familyName Castro givenName Leyla Jael
license	Visit CreativeWork Object name GPLv3 url https://www.gnu.org/licenses/gpl-3.0.en.html
citation	Quiñones N, Canales C, Torres J, Rebholz-Schuhmann D, and Castro LJ. Topic categorization for Medline Abstracts. GitHub 2022. https://github.com/zbmed-semtec/topic-categorization-system Quiñones N, Canales C, Torres J, Rebholz-Schuhmann D, Castro LJ, Aristizabal A. Multilabel-classification task for Medline abstracts - Poster. 2023. In: SWAT4HCLS 2023 poster archival. PUBLISSO. https://doi.org/10.4126/FRL01-006440395 Quiñones N, Canales C, Torres J, Rebholz-Schuhmann D, Castro LJ, Aristizabal A. Multilabel-classification task for Medline abstracts. 2023. In: SWAT4HCLS 2023 Proceedings. CEUR. https://ceur-ws.org/Vol-3415/paper-36.pdf
codeRepository	https://github.com/zbmed-semtec/topic-categorization-system
programmingLanguage	Python
keywords	Topic categorization system Biomedical literature

TREC-doc-2-doc-relevance

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name	TREC-doc-2-doc-relevance
description	The code, data and docs at this repo aim at facilitating the creation of a doc-2-doc relevance assessment on PMIDs used in the TREC 2005 Genomics track. A doc-2-doc relevance assessment takes one document as reference and assess a second document regarding its relevance to the reference one. This doc-2-doc collection will be used to evaluate the doc-2-doc recommendations approaches that we are working on.
url	https://github.com/zbmed-semtec/TREC-doc-2-doc-relevance
author	Visit Person Object familyName Talha givenName Muhammad Visit Person Object familyName Geist givenName Lukas Visit Person Object familyName Fellerhof givenName Tim Visit Person Object familyName Ravinder givenName Rohitha Visit Person Object familyName Giraldo givenName Olga Visit Person Object familyName Rebholz-Schuhmann givenName Dietrich Visit Person Object familyName Castro givenName Leyla Jael
license	Visit CreativeWork Object name MIT License url https://opensource.org/license/mit/
citation	Talha M, Geist L, Fellerhof T, Ravinder R, Giraldo O, Rebholz-Schuhmann D, and Castro LJ. (2022). TREC-doc-2-doc-relevance assessment interface (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7341391
codeRepository	https://github.com/zbmed-semtec/TREC-doc-2-doc-relevance
programmingLanguage	Python
keywords	doc-2-doc Text REtrieval Conference (TREC) 2005 TREC 2005 Relevance assessment

Zbmed-semtec.github.io

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name	Zbmed-semtec.github.io
description	We are a multidisciplinary Research and Development team combining semantic technologies and data analytics. We work on the development of softare components and services to support and improve research on information retrieval, data science and literature-based knowledge discovery with a particular focus on reproducibility. Our areas of application include the evaluation of experimental retrieval and recommendation systems, practical support to FAIR+R principles for software and data science, and data analytics from the combination of ontologies and literature-extracted data in the Life Sciences domain.
url	https://github.com/zbmed-semtec/zbmed-semtec.github.io
author	Visit Person Object familyName Lahoria givenName Honey Visit Person Object familyName Castro givenName Leyla Jael
license	Visit CreativeWork Object name MIT License url https://opensource.org/license/mit/
codeRepository	https://github.com/zbmed-semtec/zbmed-semtec.github.io
programmingLanguage	Python MKDocs
keywords	ZB MED SemTec Research team GitHub pages